MET-AICE v1.0: an operational data-driven sea ice prediction system for the European Arctic

Palerme, Cyril; Röhrs, Johannes; Lavergne, Thomas; Rusin, Jozef; Kvanum, Are Frode; Macdonald Sørensen, Atle; Melsom, Arne; Brajard, Julien; Idžanović, Martina; Durán Moro, Marina; Müller, Malte

doi:10.5194/gmd-18-9751-2025

Articles | Volume 18, issue 23

https://doi.org/10.5194/gmd-18-9751-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-18-9751-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 23

Development and technical paper

|

08 Dec 2025

Development and technical paper |

| 08 Dec 2025

MET-AICE v1.0: an operational data-driven sea ice prediction system for the European Arctic

Cyril Palerme, Johannes Röhrs, Thomas Lavergne, Jozef Rusin, Are Frode Kvanum, Atle Macdonald Sørensen, Arne Melsom, Julien Brajard, Martina Idžanović, Marina Durán Moro, and Malte Müller

Data sets

Product User Manual for Arctic Ocean Physical and BGC Analysis and Forecasting Products A. Ali et al. https://doi.org/10.48670/moi-00001

Model code and software

Code accompagning the article "MET-AICE v1.0: an operational data-driven sea ice prediction system for the European Arctic" C. Palerme https://doi.org/10.5281/zenodo.17327120

Short summary

We present MET-AICE, a sea ice prediction system based on artificial intelligence techniques that has been running operationally since March 2024. The forecasts are produced daily and provide sea ice concentration predictions for the next 10 days. We evaluate the MET-AICE forecasts from the first year of operation, and we compare them to forecasts produced by three physically-based models. We show that MET-AICE is skillful and provides more accurate forecasts than the physically-based models.